Fig. 1

Modeling pipeline for simulating effect of changes in Aβ40 and Aβ42 concentrations on cognitive outcome. For each value of Aβ40 and Aβ42 (monomer, dimer and trimer) levels (left matrix), the effect on changes in excitatory–excitatory (e-e) NMDA conductance and α7 nAChR activation is derived from the coupling equations (top and bottom graphs, respectively) and the outcome on a cognitive neuronal network is calculated for the whole 17 × 17 matrix. Top figure shows the relationship between Aβ40 and Aβ42 load and e-e NMDA conductance with maximum value δ at position x₀ and slopes α and α*. Bottom figure illustrates the relationship between Aβ load and α7 nAChR inactivation with slope β. We calculate the cognitive outcome matrix for a baseline value and for six different disease states (MCI, and mild-to-moderate AD patients at time 0, 12, 26, 52 and 78 weeks). Aβ load (left matrix) of an AD patient can be defined for any region, such as the one in gray which shows a possible low Aβ load case (the high Aβ load case would be everything outside the gray area) and its cognitive status can be determined by averaging the range of results at a particular time (the gray line in the right graph). Similarly, for any change in Aβ40 and Aβ42 low-order aggregates load as a consequence of disease pathology or therapeutic interventions over time span ΔT (going from one box to another box in the Aβ load matrix), corresponding changes in ADAS-Cog can be calculated based on their impact on cognitive readout changes using their glutamate (Glu) and α7 nAChR-mediated effects. Aβ amyloid-beta, ADAS-Cog Alzheimer Disease Assessment Scale, cognitive subscale, NMDA N-methyl-d-aspartate